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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowpark Python code snippet intended to operationalize a stored procedure:

This code results in an error during registration. Which of the following actions would be MOST appropriate to fix the registration issue and ensure the stored procedure can be successfully operationalized?

A) Add in sproc.register.
B) Define 'session' as a global variable before calling 'session.sproc.register' .
C) Remove the 'input_data' parameter from the 'main' function definition and access the input data directly using within the function.
D) Move the 'session.sproc.register' call outside of the 'main' function and execute it separately after the 'main' function definition.
E) Add 'session.close()' at the end of the main function.

2. You have a Python dictionary 'data' representing configuration settings for your Snowpark application. You need to convert this dictionary into a Snowpark DataFrame with a single row and two columns named 'Setting' and 'Value'. The 'Setting' column should contain the keys from the dictionary, and the 'Value' column should contain the corresponding values. The DataFrame needs to be created efficiently and ensure string representation of both the setting and value. Which approach is most suitable, ensuring correctness and conciseness?

A) python import snowflake.snowpark.types as T settings = list(data.items()) schema = T.StructType([T.StructField('Setting', T.StringType()), T.StructField('Value', T.StringType())]) df = session.createDataFrame(settings, schema=schema)
B) 'python settings = [1k, v] for k, v in data.items()] df = session.createDataFrame(settings, schema=['Setting', 'Value'])
C) python settings = [1k, str(v)] for k, v in data.items()] schema = ['Setting', 'Value'] df = session.createDataFrame(settings, schema=schema)
D) python import pandas as pd pd_df = pd.DataFrame(data.items(), columns=['Setting', 'Value')) df = session.createDataFrame(pd_df)
E) 'python settings = [{'Setting': k, 'Value': v} for k, v in data.items()] df = session.createDataFrame(settings)

3. Consider the following Snowpark Python code snippet designed to calculate a custom metric on financial data, using a vectorized UDF for performance. Identify potential performance bottlenecks and recommend optimization strategies.

Which of the following actions (may be more than one) would MOST likely improve the performance of this Snowpark application?

A) Change 'FloatType' to 'DoubleType' as it provides more precision.
B) Use to explicitly declare the Pandas dependency.
C) Ensure that the 'financial_data' table is clustered by 'ticker_symbor to optimize the 'groupBy' operation.
D) Repartition the 'data' DataFrame by 'ticker_symbol' before the 'groupBy' operation using to improve data locality.
E) Use before writing to the 'metric_table' , especially if the table is subsequently used downstream.

4. You are developing a Snowpark application that uses a UDTF written in Python to perform complex data transformations. The UDTF takes several input columns and returns multiple output columns. The data volume is very large. You observe performance bottlenecks during the UDTF execution. Which of the following strategies could you employ to optimize the performance of your UDTF? (Select TWO)

A) Increase the warehouse size used for the Snowpark session to provide more computational resources.
B) Use a scalar UDF instead of a UDTF to simplify the code and reduce overhead.
C) Reduce the number of input columns passed to the UDTF by performing some pre-processing outside the UDTF.
D) Employ vectorized operations within the UDTF using libraries like NumPy or pandas to process data in batches.
E) Avoid using UDTFs altogether and rewrite the transformation logic using built-in Snowpark DataFrame transformations, even if it makes the code significantly more complex.

5. Consider a JSON structure representing product information, where prices are stored as strings due to inconsistent data quality. You need to calculate the average price of products. However, some price strings contain non-numeric characters (e.g., '$', commas). Which of the following approaches, using Snowpark DataFrame operations, is the MOST robust and efficient way to clean and cast the price data to a numeric type for accurate average calculation?

A)

B)

C)

D)

E)

Solutions: